ACE-TA

An Agentic Teaching Assistant for Grounded Q&A, Quiz Generation, and Code Tutoring

Authors

  • Himanshu Tripathi University of Alabama, Tuscaloosa https://orcid.org/0009-0009-0380-0736
  • Charlotte Crowell University of Alabama, Tuscaloosa
  • Kaley Newlin Brown University
  • Subash Neupane Meharry Medical College https://orcid.org/0000-0001-9260-3914
  • Shahram Rahimi University of Alabama, Tuscaloosa
  • Jason Keith Iowa State University

DOI:

https://doi.org/10.32473/flairs.39.1.141828

Abstract

We introduce ACE-TA, the Agentic Coding and Explanations Teaching Assistant framework, that autonomously routes conceptual queries drawn from programming course material to grounded Q&A, stepwise coding guidance, and automated quiz generation using pre-trained Large Language Models (LLMs). ACE- TA consists of three coordinated modules: a retrieval- grounded conceptual Q&A system that provides precise, context-aligned explanations; a quiz generator that constructs adaptive, multi-topic assessments targeting higher-order understanding; and an interactive code tutor that guides students through step-by-step reasoning with sandboxed execution and iterative feedback.

Florida Artificial Intelligence Research Society Logo

Downloads

Published

06-05-2026

How to Cite

Tripathi, H., Crowell, C., Newlin, K., Neupane, S., Rahimi, S., & Keith, J. (2026). ACE-TA: An Agentic Teaching Assistant for Grounded Q&A, Quiz Generation, and Code Tutoring. The International FLAIRS Conference Proceedings, 39(1). https://doi.org/10.32473/flairs.39.1.141828

Issue

Vol. 39 No. 1 (2026): Proceedings of FLAIRS-39

Section

Main Track

License

Copyright (c) 2026 Himanshu Tripathi, Charlotte Crowell, Kaley Newlin, Subash Neupane, Shahram Rahimi, Jason Keith

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.